The number of media options available to individuals is increasing at an exponential pace. As the number of channels available to television viewers has increased, for example, along with the diversity of the programming content available on such channels, it has become increasingly challenging for television viewers to identify television programs of interest. Historically, television viewers identified television programs of interest by analyzing printed television program guides. Typically, such printed television program guides contained grids listing the available television programs by time and date, channel and title. As the number of television programs has increased, it has become increasingly difficult to effectively identify desirable television programs using such printed guides.
More recently, television program guides have become available in an electronic format, often referred to as electronic program guides (EPGs). Like printed television program guides, EPGs contain grids listing the available television programs by time and date, channel and title. Some EPGs, however, allow television viewers to sort or search the available television programs in accordance with personalized preferences. In addition, EPGs allow for on-screen presentation of the available television programs.
While EPGs allow viewers to identify desirable programs more efficiently than conventional printed guides, they suffer from a number of limitations, which if overcome, could further enhance the ability of viewers to identify desirable programs. For example, many viewers have a particular preference towards, or bias against, certain categories of programming, such as action-based programs or sports programming. Thus, the viewer preferences can be applied to the EPG to obtain a set of recommended programs that may be of interest to a particular viewer.
Thus, a number of tools have been proposed or suggested for recommending television programming. The Tivo™ system, for example, commercially available from Tivo, Inc., of Sunnyvale, Calif., allows viewers to rate shows using a “Thumbs Up and Thumbs Down” feature and thereby indicate programs that the viewer likes and dislikes, respectively. In this manner, the Tivo™ system implicitly derives the viewer's preferences from previous television programs that the viewer liked or did not like. Thereafter, the TiVo receiver matches the recorded viewer preferences with received program data, such as an EPG, to make recommendations tailored to each viewer.
Implicit television program recommenders generate television program recommendations based on information derived from the viewing history of the viewer, in a non-obtrusive manner. Explicit television program recommenders, on the other hand, explicitly question viewers about their preferences for program features, such as title, genre, actors, channel and date/time, to derive viewer profiles and generate recommendations.
While such television program recommenders identify programs that are likely of interest to a given viewer, they suffer from a number of limitations, which if overcome, could further improve the quality of the generated program recommendations. For example, the Tivo™ system obtains an explicit indication from the viewer of whether a given watched program was liked or disliked, which is then used to derive the viewing preferences of the user. The Tivo™ system depends on the affirmative action of the user to indicate whether a given watched program was liked or disliked, using the “Thumbs Up” or “Thumbs Down” indicator.
If the user fails to affirmatively indicate whether a given watched program was liked or disliked, the Tivo™ system will assume that the user did not like the watched program. Thus, the Tivo™ system may make false assumptions regarding the viewing preference information associated with the viewing session. In addition, the Tivo™ system typically requires the user to enter the “Thumbs Up” or “Thumbs Down” indicator using the remote control or set-top terminal, which may not be readily accessible or convenient.
A need therefore exists for a method and apparatus for obtaining feedback from a user that can determine or infer whether a given user liked or disliked certain content based on the behavior of the user. A further need exists for a method and apparatus for evaluating the reaction of a viewer to presented content in real-time and for deriving whether or not the viewer liked or disliked the presented content. Yet another need exists for a method and apparatus for a recommendation system that permits the user to indicate the strength of the user's preferences. Finally, a need exists for a method and apparatus for evaluating the reaction of a viewer to presented content that derives the viewing preferences of the user from audio or video information, or both, rather than requiring a manual entry using a specific device.